A wearable brain-computer interface controlled robot
Azhar, H., Badicioiu, A. and Barton, T. 2016. A wearable brain-computer interface controlled robot.
|Authors||Azhar, H., Badicioiu, A. and Barton, T.|
Brain-computer interface (BCI) controlled systems hold great potential for clinical applications especially in assisting neurologically disabled patients to improve their communication processes . Wearable electroencephalogram devices (EEG) are non-intrusive, meaning they do not require insertion of electrodes into the patient’s brain, and are available “off the shelf” with consumer-grade devices such as the MindWave . While such EEG devices do not possess the same high resolution EEG capabilities of medical grade devices, their affordability does make the technology accessible to new applications, such as robotics control and mood deduction , and their wearable nature negates the need for invasive surgery. Campbell et al.  investigated the potential for wearable consumer grade EEG in creating a BCI. Their aim was to implement a BCI for simple mobile phone operation, which found that a simple task, winking, could be deduced from raw data with a relatively high accuracy, and with processing being performed on a smartphone device . In doing this several limitations were highlighted with EEG devices, including a poor signal-to-noise ratio, which requires further processing to deduce useful information from raw data. Millan et al. achieved relatively sophisticated control of a mobile robot in a simulated environment with a non-intrusive BCI interface . Combining machine learning with subject-device training, they were able to achieve “almost as efficient as manual control”.
|Conference||Research and Knowledge Exchange Conference 2016|
|Publication process dates|
|Deposited||01 Dec 2016|
|Completed||22 Sep 2016|
|Accepted||22 Sep 2016|
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